LRMoE: memory-aware MoE training reduces backward demand and balances retained workload on memory-constrained nodes
Read the original at arxiv.org→arXiv:2610.37720v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models offer scalable capacity for Web applications, but uneven routing creates computation and communication bottlenecks. Existing...
Original headline: "LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes"
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- Oct 30, 04:00 UTC arXiv cs.AI lead source LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes